Anonymization; Big data; Copy problem; Data exchange; Marketplace; Platform; Secure computation
Abstract :
[en] IoT data markets in public and private institutions have become increasingly relevant in recent years because of their potential to improve data availability and unlock new business models. However, exchanging data in markets bears considerable challenges related to disclosing sensitive information. Despite considerable research focused on different aspects of privacy-enhancing data markets for the IoT, none of the solutions proposed so far seems to find a practical adoption. Thus, this study aims to organize the state-of-the-art solutions, analyze and scope the technologies that have been suggested in this context, and structure the remaining challenges to determine areas where future research is required. To accomplish this goal, we conducted a systematic literature review on privacy enhancement in data markets for the IoT, covering 50 publications dated up to July 2020, and provided updates with 24 publications dated up to May 2022. Our results indicate that most research in this area has emerged only recently, and no IoT data market architecture has established itself as canonical. Existing solutions frequently lack the required combination of anonymization and secure computation technologies. Furthermore, there is no consensus on the appropriate use of blockchain technology for IoT data markets and a low degree of leveraging existing libraries or reusing generic data market architectures. We also identified significant challenges remaining, such as the copy problem and the recursive enforcement problem that - while solutions have been suggested to some extent - are often not sufficiently addressed in proposed designs. We conclude that privacy-enhancing technologies need further improvements to positively impact data markets so that, ultimately, the value of data is preserved through data scarcity and users' privacy and businesses-critical information are protected.
Li, Y.N., Feng, X., Xie, J., Feng, H., Guan, Z., Wu, Q., A decentralized and secure blockchain platform for open fair data trading. Concurr. Comput. 32:7 (2019), 1–11, 10.1002/cpe.5578.
Hynes, N., Dao, D., Yan, D., Cheng, R., Song, D., A demonstration of sterling: A privacy-preserving data marketplace. Proc. VLDB Endow. 11:12 (2018), 2086–2089, 10.14778/3229863.3236266.
López, D., Farooq, B., A multi-layered blockchain framework for smart mobility data-markets. Transp. Res. C 111:June 2019 (2020), 588–615, 10.1016/j.trc.2020.01.002.
Liang, F., Yu, W., An, D., Yang, Q., Fu, X., Zhao, W., A survey on big data market: Pricing, trading and protection. IEEE Access 6:May (2018), 15132–15154, 10.1109/ACCESS.2018.2806881.
Bogdanov, D., Jagomägis, R., Laur, S., A universal toolkit for cryptographically secure privacy-preserving data mining. LNCS 7299 - Intelligence and Security Informatics, Vol. 7299, 2012 URL https://link.springer.com/content/pdf/10.1007%2F978-3-642-30428-6.pdf.
Spiekermann, S., Novotny, A., A vision for global privacy bridges: Technical and legal measures for international data markets. Comput. Law Secur. Rev. 31:2 (2015), 181–200, 10.1016/j.clsr.2015.01.009.
Niu, C., Zheng, Z., Wu, F., Gao, X., Chen, G., Achieving data truthfulness and privacy preservation in data markets. IEEE Trans. Knowl. Data Eng. 31:1 (2019), 105–119, 10.1109/TKDE.2018.2822727 arXiv:1812.03280.
Schomakers, E.M., Lidynia, C., Ziefle, M., All of me? Users’ preferences for privacy-preserving data markets and the importance of anonymity. Electron. Mark., 2020, 10.1007/s12525-020-00404-9.
Li, Y., Miao, C., Su, L., Gao, J., Li, Q., Ding, B., Qin, Z., Ren, K., An efficient two-layer mechanism for privacy-preserving truth discovery. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018, 1705–1714, 10.1145/3219819.3219998.
Dorri, A., Kanhere, S.S., Jurdak, R., Gauravaram, P., Blockchain for IoT security and privacy: The case study of a smart home. 2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017, 2017, IEEE, 618–623, 10.1109/PERCOMW.2017.7917634.
Li, R., Song, T., Mei, B., Li, H., Cheng, X., Sun, L., Blockchain for large-scale internet of things data storage and protection. IEEE Trans. Serv. Comput. 12:5 (2019), 762–771, 10.1109/TSC.2018.2853167.
Wei, J., Sabonuchi, M., Roche, R., Blockchain-enabled peer-to-peer data trading mechanism. 2018 IEEE International Conference on Internet of Things (IThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2018, 1349–1354, 10.1109/Cybermatics.
Zheng, Z., Mao, W., Wu, F., Chen, G., Challenges and opportunities in IoT data markets. SocialSense 2019 - Proceedings of the 2019 4th International Workshop on Social Sensing, 2019, 1–2, 10.1145/3313294.3313378.
Khalili, M.M., Zhang, X., Liu, M., Contract design for purchasing private data using a biased differentially private algorithm. Proceedings of NetEcon 2019: 14th Workshop on the Economics of Networks, Systems and Computation - in Conjunction with ACM EC 2019 and ACM SIGMETRICS 2019, 2019, 10.1145/3338506.3340273.
Yang, L., Zhang, M., He, S., Li, M., Zhang, J., Crowd-empowered privacy-preserving data aggregation for mobile crowdsensing. Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), 2018, 151–160, 10.1145/3209582.3209598.
Mišura, K., Žagar, M., Data marketplace for internet of things. Proceedings of 2016 International Conference on Smart Systems and Technologies, SST 2016, 2016, IEEE, 255–260, 10.1109/SST.2016.7765669.
Zheng, X., Data trading with differential privacy in data market. ACM Int. Conf. Proc. Ser.(8), 2020, 112–115, 10.1145/3379247.3379271.
Pennekamp, J., Henze, M., Schmidt, S., Niemietz, P., Fey, M., Trauth, D., Bergs, T., Brecher, C., Wehrle, K., Dataflow challenges in an internet of production. ACM Workshop on Cyber–Physical Systems Security & Privacy (CPS-SPC’19), November 11, 2019, 2019, ACM, London, United Kingdom, 27–38, 10.1145/3338499.3357357.
Wang, Z.J., Lin, C.H.V., Yuan, Y.H., Huang, C.C.J., Decentralized data marketplace to enable trusted machine economy. 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019, 2019, IEEE, 246–250, 10.1109/ECICE47484.2019.8942729.
Guerriero, M., Tamburri, D.A., Di Nitto, E., Defining, enforcing and checking privacy policies in data-intensive applications. Proceedings - International Conference on Software Engineering, 2018, 172–182, 10.1145/3194133.3194140.
Shi, M., Qiao, Y., Wang, X., Differentially private auctions for private data crowdsourcing. Proceedings - 2019 IEEE Intl Conf on Parallel and Distributed Processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking, ISPA/BDCloud/SustainCom/SocialCom 2019, 2019, IEEE, 1–8, 10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00013.
Du, J., Jiang, C., Gelenbe, E., Xu, L., Li, J., Ren, Y., Distributed data privacy preservation in IoT applications. IEEE Wirel. Commun. 25:December (2018), 68–76, 10.1109/MWC.2017.1800094.
Gao, G., Xiao, M., Wu, J., Zhang, S., Huang, L., Xiao, G., DPDT: A differentially private crowd-sensed data trading mechanism. IEEE Internet Things J. 7:1 (2020), 751–762, 10.1109/JIOT.2019.2944107.
Cheng, R., Zhang, F., Kos, J., He, W., Hynes, N., Johnson, N., Juels, A., Miller, A., Song, D., Ekiden: A platform for confidentiality-preserving, trustworthy, and performant smart contracts. Proceedings - 4th IEEE European Symposium on Security and Privacy, EURO S and P 2019, 2019, 185–200, 10.1109/EuroSP.2019.00023.
Perera, C., Ranjan, R., Wang, L., End-to-end privacy for open big data markets. IEEE Cloud Comput. 2:4 (2015), 44–53, 10.1109/MCC.2015.78.
Zichichi, M., Contu, M., Ferretti, S., Rodríguez-Doncel, V., Ensuring personal data anonymity in data marketplaces through sensing-as-a-service and distributed ledger technologies. CEUR Workshop Proceedings, Vol. 2580, 2020 URL https://www.researchgate.net/publication/340183476_Ensuring_Personal_Data_Anonymity_in_Data_Marketplaces_through_Sensing-as-a-Service_and_Distributed_Ledger.
Duri, S., Gruteser, M., Liu, X., Moskowitz, P., Perez, R., Singh, M., Tang, J.M., Framework for security and privacy in automotive telematics. Proceedings of the ACM International Workshop on Mobile Commerce, 2002, 25–32, 10.1145/570709.570711.
Tzianos, P., Pipelidis, G., Tsiamitros, N., Hermes: An open and transparent marketplace for iot sensor data over distributed ledgers. ICBC 2019 - IEEE International Conference on Blockchain and Cryptocurrency, 2019, IEEE, 167–170, 10.1109/BLOC.2019.8751331.
Li, K., Tian, L., Li, W., Luo, G., Cai, Z., Incorporating social interaction into three-party game towards privacy protection in iot. Computer Networks 150 (2019), 90–101, 10.1016/j.comnet.2018.11.036.
Zhao, Y., Yu, Y., Li, Y., Han, G., Du, X., Machine learning based privacy-preserving fair data trading in big data market. Inform. Sci. 478 (2019), 449–460, 10.1016/j.ins.2018.11.028.
Kiyomoto, S., Rahman, M.S., Basu, A., On blockchain-based anonymized dataset distribution platform. 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA), 2017, IEEE, 85–92 URL https://ieeexplore.ieee.org/document/7965711.
Chen, Z., Chen, L., Huang, L., Zhong, H., On privacy-preserving cloud auction. Proceedings of the IEEE Symposium on Reliable Distributed Systems, 2016, IEEE, 279–288, 10.1109/SRDS.2016.045.
Sánchez, D., Viejo, A., Personalized privacy in open data sharing scenarios. Online Inf. Rev. 41:3 (2017), 298–310, 10.1108/OIR-01-2016-0011.
Jung, K., Park, S., Privacy bargaining with fairness: Privacy-price negotiation system for applying differential privacy in data market environments. 2019 IEEE International Conference on Big Data, 2019, IEEE, 1389–1394, 10.1109/BigData47090.2019.9006101.
Ziegeldorf, J.H., Morchon, O.G., Wehrle, K., Privacy in the internet of things: Threats and challenges. Secur. Commun. Netw. 7:12 (2014), 2728–2742, 10.1002/sec.795.
Perera, C., McCormick, C., Bandara, A.K., Price, B.A., Nuseibeh, B., Privacy-by-design framework for assessing internet of things applications and platforms. ACM Int. Conf. Proc. Ser. 07-09-Nove (2016), 83–92, 10.1145/2991561.2991566.
Perera, C., Liu, C., Ranjan, R., Wang, L., Zomaya, A., Privacy-knowledge modeling for the internet of things: A look back. Computer 49:12 (2016), 60–68, 10.1109/MC.2016.366.
Gao, W., Yu, W., Liang, F., Hatcher, W.G., Lu, C., Privacy-preserving auction for big data trading using homomorphic encryption. IEEE Trans. Netw. Sci. Eng. 7:2 (2020), 776–791, 10.1109/TNSE.2018.2846736.
Park, S., Park, K., Lee, J., Jung, K., PRIVATA: Differentially private data market framework using negotiation-based pricing mechanism. Proceedings of ACM CIKM Conference (CIKM’19), November 3–7, 2019, Beijing, China, 2019, 156–157, 10.1007/978-3-663-10915-0_47.
Koutsos, V., Papadopoulos, D., Chatzopoulos, D., Tarkoma, S., Hui, P., Agora: A privacy-aware data marketplace. 2020, 13 URL https://eprint.iacr.org/2020/865.pdf.
Cao, J., Karras, P., Publishing microdata with a robust privacy guarantee. Proc. VLDB Endow. 5:11 (2012), 1388–1399, 10.14778/2350229.2350255 arXiv:1208.0220.
Dai, W., Dai, C., Choo, K.K.R., Cui, C., Zou, D., Jin, H., SDTE: A secure blockchain-based data trading ecosystem. IEEE Trans. Inf. Forensics Secur. 15 (2020), 725–737, 10.1109/TIFS.2019.2928256.
Guan, Z., Shao, X., Wan, Z., Secure, fair and efficient data trading without third party using blockchain. 2018 IEEE International Conference on Internet of Things (IThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2018, IEEE, 1349–1354 doi:10.1109/Cybermatics.
Alsheikh, M.A., Jiao, Y., Niyato, D., Wang, P., Leong, D., Han, Z., The accuracy-privacy trade-off of mobile crowdsensing. IEEE Commun. Mag. 55:6 (2017), 132–139, 10.1109/MCOM.2017.1600737 arXiv:1702.04565.
Sharma, S., Chen, K., Sheth, A., Toward practical privacy-preserving analytics for IoT and cloud-based healthcare systems. IEEE Internet Comput. 22:2 (2018), 42–51, 10.1109/MIC.2018.112102519.
Colman, A., Chowdhury, M.J.M., Baruwal Chhetri, M., Toward a trusted marketplace for wearable data. Proceedings - 2019 IEEE 5th International Conference on Collaboration and Internet Computing, CIC 2019 (Cic), 2019, 314–321, 10.1109/CIC48465.2019.00044.
Cai, Z., He, Z., Trading private range counting over big IoT data. Proceedings - International Conference on Distributed Computing Systems, Vol. 2019-July, 2019, IEEE, 144–153, 10.1109/ICDCS.2019.00023.
IDC, Open Evidence. European data market SMART. 2017 URL https://ec.europa.eu/newsroom/dae/document.cfm?doc_id=44400.
Miller, A.R., Tucker, C., Health information exchange, system size and information silos. 2013, 29 URL https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1457719.
Stahl, F., Schomm, F., Vossen, G., Vomfell, L., A classification framework for data marketplaces. Vietnam J. Comput. Sci. 3:3 (2016), 137–143, 10.1007/s40595-016-0064-2.
McKinsey & Company. Four ways to accelerate the creation of data ecosystems. 2020 https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/four-ways-to-accelerate-the-creation-of-data-ecosystems.
Eggers, G., Fondermann, B., Maier, B., Ottradovetz, K., Pformmer, J., Reinhardt, R., Rollin, H., Schmieg, A., Steinbuß, S., Trinius, P., Weis, A., Weiss, C., Wilfling, S., GAIA-X: Technical architecture. 2020 URL https://www.data-infrastructure.eu/GAIAX/Redaktion/EN/Publications/gaia-x-technical-architecture.pdf?__blob=publicationFile&v=5.
Sweeney, L., Abu, A., Winn, J., Identifying participants in the personal genome project by name. SSRN Electron. J., 2013, 10.2139/ssrn.2257732.
Gao, X., Firner, B., Sugrim, S., Kaiser-Pendergrast, V., Yang, Y., Lindqvist, J., Elastic pathing: your speed is enough to track you. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp ’14 Adjunct, 2014, ACM Press, Seattle, Washington, 975–986, 10.1145/2632048.2632077.
Sunyaev, A., Kannengießer, N., Beck, R., Treiblmaier, H., Lacity, M., Kranz, J., Fridgen, G., Spankowski, U., Luckow, A., Token economy. Bus. Inf. Syst. Eng., 2021 URL https://link.springer.com/article/10.1007/s12599-021-00684-1.
IBM Security and Ponemon Institute LLC. 2018 Cost of a data breach study: Global overview. 2018, 47 URL https://www.intlxsolutions.com/hubfs/2018_Global_Cost_of_a_Data_Breach_Report.pdf.
Trask, A., Bluemke, E., Garfinkel, B., Cuervas-Mons, C.G., Dafoe, A., Beyond privacy trade-offs with structured transparency. 2020 arXiv:2012.08347. URL https://www.researchgate.net/publication/347300876_Beyond_Privacy_Trade-offs_with_Structured_Transparency.
Hes, R., Borking, J.J., Netherlands, Commissioner/Ontario, I.a.P., (eds.) Privacy-enhancing Technologies: the Path to Anonymity, rev. ed. Achtergrondstudies en Verkenningen, 1998, Registratiekamer, The Hague URL https://www.researchgate.net/publication/243777645_Privacy-Enhancing_Technologies_The_Path_to_Anonymity.
Dwork, C., McSherry, F., Nissim, K., Smith, A., Calibrating noise to sensitivity in private data analysis. Halevi, S., Rabin, T., (eds.) Theory of Cryptography, 2006, Springer Berlin Heidelberg, Berlin, Heidelberg, 265–284 URL https://link.springer.com/chapter/10.1007/11681878_14. Online; accessed 30 December 2021.
Dwork, C., Roth, A., The algorithmic foundations of differential privacy. Found. Trends® Theor. Comput. Sci. 9:3–4 (2013), 211–407, 10.1561/0400000042.
P. Samarati, L. Sweeney, 1998. Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. 19. URL https://epic.org/privacy/reidentification/Samarati_Sweeney_paper.pdf.
Will, M.A., Ko, R.K., A Guide to Homomorphic Encryption. 2015, Elsevier Inc., 101, 10.1016/B978-0-12-801595-7.00005-7.
Chaudhary, P., Gupta, R., Singh, A., Majumder, P., Analysis and comparison of various fully homomorphic encryption techniques. 2019 International Conference on Computing, Power and Communication Technologies, GUCON 2019, 2019, Galgotias University, 58–62 URL https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8940577.
Paillier, P., Public-key cryptosystems based on composite degree residuosity classes. Eurocrypt, 1999, 10.1007/3-540-48910-X_9.
Yao, A.C., Protocols for secure computations. 23rd Annual Symposium on Foundations of Computer Science (Sfcs 1982), 1982, IEEE, Chicago, IL, USA, 160–164, 10.1109/SFCS.1982.38.
Goldwasser, S., Micali, S., Rackoff, C., The knowledge complexity of interactive proof systems. SIAM J. Comput. 18:1 (1989), 186–208, 10.1137/0218012.
Goldreich, O., Oren, Y., Definitions and properties of zero-knowledge proof systems. J. Cryptol. 7:1 (1994), 1–32, 10.1007/BF00195207.
G. Bondel, G.M. Garrido, K. Baumer, F. Matthes, 2020. Towards a privacy-enhancing tool based on de- identification methods. 8. URL https://aisel.aisnet.org/pacis2020/157/.
Spiekermann, S., Böhme, R., Acquisti, A., Hui, K.-L., Personal data markets. Electron. Mark. 25:2 (2015), 91–93, 10.1007/s12525-015-0190-1.
Anne Zöll, P.B., Olt, C.M., Privacy-sensitive business models: Barriers of organizational adoption of privacy-enhancing technologies. 2021, 22 URL https://aisel.aisnet.org/ecis2021_rp/34/.
Westin, A.F., Privacy and Freedom. 1967, IG Publishing, New York URL https://scholarlycommons.law.wlu.edu/wlulr/vol25/iss1/20/.
Fink, G.A., Song, H., Jeschke, S., (eds.) Security and Privacy in Cyber-Physical Systems: Foundations, Principles, and Applications, first ed., 2018, Wiley IEEE Press, Hoboken, NJ URL https://ieeexplore.ieee.org/servlet/opac?bknumber=8068866.
Renaud, K., Galvez-Cruz, D., Privacy: Aspects, definitions and a multi-faceted privacy preservation approach. Proceedings of the 2010 Information Security for South Africa Conference, ISSA 2010, 2010, 1–8, 10.1109/ISSA.2010.5588297.
Solove, D.J., The meaning and value of privacy. Roessler, B., Mokrosinska, D., (eds.) Social Dimensions of Privacy, 2015, Cambridge University Press, Cambridge, 71–82, 10.1017/CBO9781107280557.005.
Wu, F.T., Defining privacy and utility in data sets. 84 University of Colorado Law Review 1117 (2013); 2012 TRPC, 2012, 1117–1177, 10.2139/ssrn.2031808.
Deng, M., Wuyts, K., Scandariato, R., Preneel, B., Joosen, W., A privacy threat analysis framework: supporting the elicitation and fulfillment of privacy requirements. Requir. Eng. 16:1 (2011), 3–32, 10.1007/s00766-010-0115-7.
Garratt, R., Oordt, M.R.v., Privacy as a public good: A case for electronic cash. J. Polit. Econ., 2018, 10.1086/714133.
Kaaniche, N., Laurent, M., Attribute-based signatures for supporting anonymous certification. Askoxylakis, I., Ioannidis, S., Katsikas, S., Meadows, C., (eds.) Computer Security – ESORICS 2016, 2016, Springer International Publishing, Cham, 279–300 URL https://www.semanticscholar.org/paper/Attribute-Based-Signatures-for-Supporting-Anonymous-Kaaniche-Laurent-Maknavicius/3b0624ff32b9258ca2351c894d320d83a546fcd6.
Campbell, J.E., Carlson, M., Panopticon.com: Online surveillance and the commodification of privacy. J. Broadcast. Electron. Media 46:4 (2002), 586–606, 10.1207/s15506878jobem4604_6.
Lichter, A., Löffler, M., Siegloch, S., The long-term costs of government surveillance: Insights from stasi spying in east Germany. J. Eur. Econom. Assoc. 19:2 (2020), 741–789, 10.1093/jeea/jvaa009 arXiv:https://academic.oup.com/jeea/article-pdf/19/2/741/37108669/jvaa009.pdf.
Kokolakis, S., Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon. Comput. Secur. 64 (2017), 122–134, 10.1016/j.cose.2015.07.002.
J. Coppel, E-Commerce: Impacts and Policy Challenges, OECD Economics Department Working Papers 252, 2000, http://dx.doi.org/10.1787/801315684632, Series: OECD Economics Department Working Papers Volume: 252.
Kennedy, J., Big data's economic impact. 2021 [Online]. Available: https://www.ced.org/blog/entry/big-datas-economic-impact, [Accessed on 04 Jul. 2021].
Oberländer, A.M., Röglinger, M., Rosemann, M., Kees, A., Conceptualizing business-to-thing interactions – a sociomaterial perspective on the internet of things. Eur. J. Inf. Syst. 27:4 (2018), 486–502, 10.1080/0960085X.2017.1387714.
Lee, I., Lee, K., The internet of things (IoT): Applications, investments, and challenges for enterprises. Bus. Horiz. 58:4 (2015), 431–440, 10.1016/j.bushor.2015.03.008.
Basili, V., Caldiera, G., Rombach, D., The goal question metric approach. Encycl. Softw. Eng., 1994, 528–532 URL http://www.cs.toronto.edu/~sme/CSC444F/handouts/GQM-paper.pdf.
Kitchenham, B.A., Budgen, D., Evidence-Based Software Engineering and Systematic Reviews. 2015, Chapman and Hall/CRC URL https://dl.acm.org/doi/book/10.5555/2994449.
B. Kitchenham, Procedures for Performing Systematic Reviews, Joint Technical Report, 2004, http://dx.doi.org/10.5144/0256-4947.2017.79.
Mariano, D.C.B., Leite, C., Santos, L.H.S., Rocha, R.E.O., de Melo-Minardi, R.C., A guide to performing systematic literature reviews in bioinformatics. 2017 arXiv:1707.05813.
Dybå, T., Dingsøyr, T., Hanssen, G., Applying systematic reviews to diverse study types: An experience report. Proceedings - 1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007 (7465), 2007, 126–135, 10.1109/ESEM.2007.59.
Dieste, O., Grimán, A., Juristo, N., Developing search strategies for detecting relevant experiments. Empir. Softw. Eng. 14:5 (2009), 513–539, 10.1007/s10664-008-9091-7.
Kilgarriff, A., Baisa, V., Bušta, J., Jakubíček, M., Kovář, V., Michelfeit, J., Rychlý, P., Suchomel, V., The sketch engine: ten years on. Lexicography, 2014 URL https://www.researchgate.net/publication/271848017_The_Sketch_Engine_Ten_Years_On.
Brereton, O.P., Kitchenham, B.A., Budgen, D., Turner, M., Khalil, M., Lessons from applying the systematic literature review process within the software engineering domain. J. Syst. Softw. 80:4 (2007), 571–583, 10.1016/j.jss.2006.07.009.
Kitchenham, B.A., Brereton, O.P., A systematic review of systematic review process research in software engineering. Inf. Softw. Technol. 55:12 (2013), 2049–2075, 10.1016/j.infsof.2013.07.010.
Chen, L., Babar, M.A., Zhang, H., Towards an evidence-based understanding of electronic data sources (january 2015). 2010, 10.14236/ewic/ease2010.17.
Wohlin, C., Runeson, P., Höst, M., Ohlsson, M.C., Regnell, B., Wesslén, A., Experimentation in Software Engineering. 2012, Springer Science & Business Media, 10.1007/978-3-642-29044-2.
Nissenbaum, H., Privacy in Context: Technology, Policy, and the Integrity of Social Life. 2009, Stanford University Press URL https://www.sup.org/books/title/?id=8862.
Wang, R.Y., Strong, D.M., Beyond accuracy: What data quality means to data consumers. J. Manage. Inf. Syst. 12:4 (1996), 5–33, 10.1080/07421222.1996.11518099.
Dinev, T., Xu, H., Smith, J.H., Hart, P., Information privacy and correlates: an empirical attempt to bridge and distinguish privacy-related concepts. Eur. J. Inf. Syst. 22:3 (2013), 295–316, 10.1057/ejis.2012.23.
Butijn, B.-J., Tamburri, D.A., Heuvel, W.-J.v.d., Blockchains: a systematic multivocal literature review. ACM Comput. Surv. 53:3 (2020), 1–37 URL https://dl.acm.org/doi/abs/10.1145/3369052.
Zhang, R., Xue, R., Liu, L., Security and privacy on blockchain. ACM Comput. Surv. 52:3 (2019), 1–34 URL https://dl.acm.org/doi/10.1145/3316481.
Simari, G.I., A primer on zero knowledge protocols. 2002, 12 URL http://cs.uns.edu.ar/~gis/publications/zkp-simari2002.pdf.
Kaaniche, N., Laurent, M., Belguith, S., Privacy enhancing technologies for solving the privacy-personalization paradox: Taxonomy and survey. J. Netw. Comput. Appl., 2020.
Chaum, D., Security without identification: transaction systems to make big brother obsolete. Commun. ACM 28:10 (1985), 1030–1044, 10.1145/4372.4373.
Camenisch, J.L., Piveteau, J.-M., Stadler, M.A., Blind signatures based on the discrete logarithm problem. De Santis, A., (eds.) Advances in Cryptology — EUROCRYPT’94, 1995, Springer Berlin Heidelberg, Berlin, Heidelberg, 428–432 URL https://link.springer.com/chapter/10.1007/BFb0053458.
Camenisch, J., Lysyanskaya, A., Dynamic accumulators and application to efficient revocation of anonymous credentials. Goos, G., Hartmanis, J., van Leeuwen, J., Yung, M., (eds.) Advances in Cryptology — CRYPTO 2002, Vol. 2442 Lecture Notes in Computer Science, 2002, Springer Berlin Heidelberg, Berlin, Heidelberg, 61–76, 10.1007/3-540-45708-9_5.
Brands, S.A., Rethinking Public Key Infrastructures and Digital Certificates: Building in Privacy. 2000, MIT Press, Cambridge, MA, USA URL https://direct.mit.edu/books/book/1912/Rethinking-Public-Key-Infrastructures-and-Digital.
Sedlmeir, J., Smethurst, R., Rieger, A., Fridgen, G., Digital identities and verifiable credentials. Bus. Inf. Syst. Eng. 63:5 (2021), 603–613.
Schlatt, V., Sedlmeir, J., Feulner, S., Urbach, N., Designing a framework for digital KYC processes built on blockchain-based self-sovereign identity. Inf. Manage., 2021, 103553.
Bangerter, E., Barzan, S., Krenn, S., Sadeghi, A.-R., Schneider, T., Tsay, J.-K., Bringing zero-knowledge proofs of knowledge to practice. 2009, 12 URL https://eprint.iacr.org/2009/211.pdf.
Hoffmann, M., Klooß, M., Rupp, A., Efficient zero-knowledge arguments in the discrete log setting, revisited. Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, 2019, ACM, London United Kingdom, 2093–2110, 10.1145/3319535.3354251.
Nakanishi, T., Yoshino, H., Murakami, T., Policharla, G.-V., Efficient zero-knowledge proofs of graph signature for connectivity and isolation using bilinear-map accumulator. Proceedings of the 7th ACM Workshop on ASIA Public-Key Cryptography, 2020, ACM, Taipei Taiwan, 9–18, 10.1145/3384940.3388959.
Zhang, Y., Zero-knowledge proofs for machine learning. Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice, 2020, Association for Computing Machinery, 7 URL https://doi.org/10.1145/3411501.3418608.
Stadler, M., Publicly verifiable secret sharing. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 1070, 1996, 190–199, 10.1007/3-540-68339-9_17.
Shamir, A., How to share a secret. Commun. ACM 22:11 (1979), 612–613, 10.1145/359168.359176.
Lindell, Y., Pinkas, B., A proof of security of Yao's protocol for two-party computation. J. Cryptol. 22:2 (2009), 161–188, 10.1007/s00145-008-9036-8.
Ben-David, A., Nisan, N., Pinkas, B., Fairplaymp: a system for secure multi-party computation. Proceedings of the 15th ACM Conference on Computer and Communications Security - CCS ’08, 2008, ACM Press, Alexandria, Virginia, USA, 257, 10.1145/1455770.1455804.
Yakoubov, S., A gentle introduction to Yao’ s garbled circuits. 2017 URL https://web.mit.edu/sonka89/www/papers/2017ygc.pdf.
Genç, Z.A., Iovino, V., Rial, A., The simplest protocol for oblivious transfer. Inform. Process. Lett. 161 (2020), 1–12, 10.1016/j.ipl.2020.105975.
Pullonen, P., Siim, S., Combining secret sharing and garbled circuits for efficient private IEEE 754 floating-point computations. Brenner, M., Christin, N., Johnson, B., Rohloff, K., (eds.) Financial Cryptography and Data Security, Vol. vol. 8976 Lecture Notes in Computer Science, 2015, Springer Berlin Heidelberg, Berlin, Heidelberg, 172–183, 10.1007/978-3-662-48051-9_13.
Yang, Y., Huang, X., Liu, X., Cheng, H., Weng, J., Luo, X., Chang, V., A comprehensive survey on secure outsourced computation and its applications. IEEE Access 7 (2019), 159426–159465 URL https://ieeexplore.ieee.org/document/8884162/.
Boyle, E., Gilboa, N., Ishai, Y., Nof, A., Practical fully secure three-party computation via sublinear distributed zero-knowledge proofs. Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, 2019, ACM, London United Kingdom, 869–886 URL https://dl.acm.org/doi/10.1145/3319535.3363227.
Boneh, D., Goh, E.-J., Nissim, K., Evaluating 2-DNF formulas on ciphertexts., 3378, 2005, 325–341 URL https://www.researchgate.net/publication/221354138_Evaluating_2-DNF_Formulas_on_Ciphertexts.
Nikolaenko, V., Ioannidis, S., Weinsberg, U., Joye, M., Taft, N., Boneh, D., Privacy-preserving matrix factorization. Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security, 2013, Association for Computing Machinery, 801–812 URL https://doi.org/10.1145/2508859.2516751.
Boneh, D., Sahai, A., Waters, B., Functional encryption: Definitions and challenges. Ishai, Y., (eds.) Theory of Cryptography, 2011, Springer Berlin Heidelberg, Berlin, Heidelberg, 253–273 URL https://eprint.iacr.org/2010/543.pdf.
Chotard, J., Dufour Sans, E., Gay, R., Phan, D.H., Pointcheval, D., Decentralized multi-client functional encryption for inner product. Peyrin, T., Galbraith, S., (eds.) Advances in Cryptology – ASIACRYPT 2018, 2018, Springer International Publishing, Cham, 703–732 URL https://eprint.iacr.org/2017/989.pdf.
Wang, W., Hu, Y., Chen, L., Huang, X., Sunar, B., Exploring the feasibility of fully homomorphic encryption. IEEE Trans. Comput. 64:3 (2015), 698–706, 10.1109/TC.2013.154.
Anati, I., Gueron, S., Johnson, S.P., Scarlata, V.R., Innovative technology for CPU based attestation and sealing. 2013, 7 URL https://software.intel.com/content/dam/develop/external/us/en/documents/hasp-2013-innovative-technology-for-attestation-and-sealing-413939.pdf.
Khalid, F., Masood, A., Vulnerability analysis of qualcomm secure execution environment. Comput. Secur., 116, 2022, 102628, 10.1016/j.cose.2022.102628 URL https://www.sciencedirect.com/science/article/pii/S016740482200027X.
Alder, F., Van Bulck, J., Spielman, J., Oswald, D., Piessens, F., Faulty point unit: ABI poisoning attacks on trusted execution environments. Digit. Threats Res. Pract., 3(2), 2022, 10.1145/3491264 URL https://doi-org.eaccess.ub.tum.de/10.1145/3491264.
Skarlatos, D., Yan, M., Gopireddy, B., Sprabery, R., Torrellas, J., Fletcher, C.W., MicroScope: Enabling microarchitectural replay attacks. Proceedings of the 46th International Symposium on Computer Architecture, 2019, ACM, 318–331, 10.1145/3307650.3322228.
Costan, V., Lebedev, I., Devadas, S., Sanctum: Minimal hardware extensions for strong software isolation. 2016, 19 URL https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/costan.
Lee, D., Kohlbrenner, D., Shinde, S., Asanović, K., Song, D., Keystone: an open framework for architecting trusted execution environments. Proceedings of the Fifteenth European Conference on Computer Systems, 2020, ACM, Heraklion Greece, 1–16, 10.1145/3342195.3387532.
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H.B., Patel, S., Ramage, D., Segal, A., Seth, K., Practical secure aggregation for privacy-preserving machine learning. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security CCS ’17, 2017, Association for Computing Machinery, New York, NY, USA, 1175–1191, 10.1145/3133956.3133982.
Zhang, S., Li, Z., Chen, Q., Zheng, W., Leng, J., Guo, M., Dubhe: Towards data unbiasedness with homomorphic encryption in federated learning client selection. 50th International Conference on Parallel Processing ICPP 2021, 2021, Association for Computing Machinery, New York, NY, USA, 10.1145/3472456.3473513 URL https://doi-org.eaccess.ub.tum.de/10.1145/3472456.3473513.
Yang, W., Liu, B., Lu, C., Yu, N., Privacy Preserving on Updated Parameters in Federated Learning, ACM TURC’20. 2020, Association for Computing Machinery, New York, NY, USA, 27–31, 10.1145/3393527.3393533.
Vepakomma, P., Gupta, O., Swedish, T., Raskar, R., Split learning for health: Distributed deep learning without sharing raw patient data. 2018 URL https://aiforsocialgood.github.io/iclr2019/accepted/track1/pdfs/31_aisg_iclr2019.pdf.
Gupta, O., Raskar, R., Distributed learning of deep neural network over multiple agents. J. Netw. Comput. Appl. 116 (2018), 1–8, 10.1016/j.jnca.2018.05.003.
M.G. Poirot, P. Vepakomma, K. Chang, J. Kalpathy-Cramer, R. Gupta, R. Raskar, 2019. Split learning for collaborative deep learning in healthcare. 9. URL https://arxiv.org/abs/1912.12115.
Giaretta, L., Girdzijauskas, S., Gossip learning: Off the beaten path. 2019 IEEE International Conference on Big Data (Big Data), 2019, 1117–1124, 10.1109/BigData47090.2019.9006216.
R. Ormándi, I. Hegedűs, M. Jelasity, 2012. Gossip learning with linear models on fully distributed data: Efficient p2p ensemble learning with linear models on fully distributed data. 25 (4), 556–571. http://dx.doi.org/10.1002/cpe.2858. URL https://onlinelibrary.wiley.com/doi/10.1002/cpe.2858.
Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., Zhang, L., Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 2016, ACM, 10.1145/2976749.2978318.
Domingo-Ferrer, J., Sánchez, D., Blanco-Justicia, A., The limits of differential privacy (and its misuse in data release and machine learning). Commun. ACM 64:7 (2021), 33–35, 10.1145/3433638 URL https://doi-org.eaccess.ub.tum.de/10.1145/3433638.
Dikici, E., Prevedello, L.M., Bigelow, M., White, R.D., Erdal, B.S., Constrained generative adversarial network ensembles for sharable synthetic data generation. 2020 arXiv:2003.00086. URL https://www.researchgate.net/publication/339642358_Constrained_Generative_Adversarial_Network_Ensembles_for_Sharable_Synthetic_Data_Generation.
Torfi, A., Fox, E.A., Reddy, C.K., Differentially private synthetic medical data generation using convolutional GANs. 2020 URL https://www.researchgate.net/publication/347624671_Differentially_Private_Synthetic_Medical_Data_Generation_using_Convolutional_GANs.
2010. Nin, J., Herranz, J. (Eds.), Privacy and Anonymity in Information Management Systems. In: Advanced Information and Knowledge Processing, Springer, London. http://dx.doi.org/10.1007/978-1-84996-238-4. URL http://link.springer.com/10.1007/978-1-84996-238-4.
Puri, V., Sachdeva, S., Kaur, P., Privacy preserving publication of relational and transaction data: Survey on the anonymization of patient data. Comput. Sci. Rev. 32:C (2019), 45–61, 10.1016/j.cosrev.2019.02.001.
Fung, B.C.M., Wang, K., Chen, R., Yu, P.S., Privacy-preserving data publishing: A survey of recent developments. ACM Comput. Surv., 42(4), 2010, 10.1145/1749603.1749605.
Aggarwal, C.C., Yu, P.S., (eds.) Privacy-Preserving Data Mining - Models and Algorithms Advances in Database Systems, vol. 34, 2008, Springer, 10.1007/978-0-387-70992-5.
P. Ram Mohan Rao, S. Murali Krishna, A.P. Siva Kumar, 2018. Privacy preservation techniques in big data analytics: a survey. 5 (1), 33. http://dx.doi.org/10.1186/s40537-018-0141-8. URL https://journalofbigdata.springeropen.com/articles/10.1186/s40537-018-0141-8.
Cunha, M., Mendes, R., ao P. Vilela, J., A survey of privacy-preserving mechanisms for heterogeneous data types. Comp. Sci. Rev., 41, 2021, 100403, 10.1016/j.cosrev.2021.100403 URL https://www.sciencedirect.com/science/article/pii/S1574013721000435.
Dwork, C., Smith, A., Steinke, T., Ullman, J., Exposed! A survey of attacks on private data. Annu. Rev. Stat. Appl. 4:1 (2017), 61–84, 10.1146/annurev-statistics-060116-054123.
ISO. Privacy enhancing data de-identification terminology and classification of techniques. 2018 URL https://www.iso.org/standard/69373.html.
Li, N., Qardaji, W., Su, D., On sampling, anonymization, and differential privacy or, k-anonymization meets differential privacy. Proceedings of the 7th ACM Symposium on Information, Computer and Communications Security ASIACCS ’12, 2012, Association for Computing Machinery, New York, NY, USA, 32–33, 10.1145/2414456.2414474.
Xu, H., Guo, S., Chen, K., Building confidential and efficient query services in the cloud with RASP data perturbation. 2013, 10.1109/TKDE.2012.251.
Chen, K., Liu, L., Geometric data perturbation for privacy preserving outsourced data mining. Knowl. Inf. Syst. 29:3 (2011), 657–695 URL http://link.springer.com/10.1007/s10115-010-0362-4.
Henry, R., Herzberg, A., Kate, A., Blockchain access privacy: Challenges and directions. IEEE Secur. Priv. 16:4 (2018), 38–45, 10.1109/MSP.2018.3111245.
Chaum, D., Untraceable electronic mail, return addresses, and digital pseudonyms. Commun. ACM 24:2 (1981), 84–90.
Chaum, D., The dining cryptographers problem: Unconditional sender and recipient untraceability. J. Cryptol. 1:1 (1988), 65–75.
Ren, J., Wu, J., Survey on anonymous communications in computer networks. Comput. Commun. 33:4 (2010), 420–431.
Ali, M.S., Dolui, K., Antonelli, F., Iot data privacy via blockchains and IPFS. Proceedings of the Seventh International Conference on the Internet of Things IoT ’17, 2017, Association for Computing Machinery, New York, NY, USA, 10.1145/3131542.3131563.
Kesarwani, M., Kaul, A., Braghin, S., Holohan, N., Antonatos, S., Secure k-anonymization over encrypted databases. 2021 IEEE 14th International Conference on Cloud Computing (CLOUD), 2021, 20–30, 10.1109/CLOUD53861.2021.00015.
Westin, A.F., Privacy and freedom. 1970 URL https://www.worldcat.org/title/privacy-and-freedom/oclc/792862.
Raj, A., Bosch, J., Olsson, H.H., Wang, T.J., Modelling data pipelines. 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 2020, 13–20, 10.1109/SEAA51224.2020.00014.
Cavoukian, A., The 7 foundational principles. 2011, 2 URL https://sites.psu.edu/digitalshred/2020/11/13/privacy-by-design-pbd-the-7-foundational-principles-cavoukian/.
Sedlmeir, J., Lautenschlager, J., Fridgen, G., Urbach, N., The transparency challenge of blockchain in organizations. Electron. Mark., 2022, 10.1007/s12525-022-00536-0.
Heilman, E., Narula, N., Tanzer, G., Lovejoy, J., Colavita, M., Virza, M., Dryja, T., Cryptanalysis of curl-p and other attacks on the IOTA cryptocurrency. IACR Trans. Symm. Crypt., 2020, 367–391, 10.46586/tosc.v2020.i3.367-391.
Wang, D., Zhao, J., Wang, Y., A survey on privacy protection of blockchain: The technology and application. IEEE Access 8 (2020), 108766–108781, 10.1109/ACCESS.2020.2994294 URL https://ieeexplore.ieee.org/document/9093015/.
Sedlmeir, J., Buhl, H.U., Fridgen, G., Keller, R., The energy consumption of blockchain technology: beyond myth. Bus. Inf. Syst. Eng. 62:6 (2020), 599–608 URL https://www.researchgate.net/publication/342313238_The_Energy_Consumption_of_Blockchain_Technology_Beyond_Myth.
European Parliament, N., European Parliament and Council of the European Union. Regulation (EU) 2016/679 directive 95/46/EC (general data protection regulation): General data protection regulation. Off. J. Eur. Union(L119), 2016, 1–88 URL https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32016R0679.
Ellis, S., Juels, A., Nazarov, S., Chainlink a decentralized oracle network. 2021 URL https://link.smartcontract.com/whitepaper.
Al-Riyami, S.S., Paterson, K.G., Certificateless public key cryptography. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 2894, 2003, 452–473 URL https://eprint.iacr.org/2003/126.pdf.
Reed, D., Sporny, M., Longley, D., Allen, C., Grant, A., Sabadello, M., Decentralized identifiers (dids) v1.0. 2021 URL https://w3c.github.io/did-core/.
Mui, L., Mohtashemi, M., Halberstadt, A., A computational model of trust and reputation. Proceedings of the 35th Hawaii International Conference on System Sciences, 2002, IEEE, 2431–2439.
Grandison, T., Sloman, M., A survey of trust in internet applications. IEEE Commun. Surv. Tutor. 3:4 (2000), 2–16.
Artz, D., Gil, Y., A survey of trust in computer science and the semantic web. J. Web Semant. 5:2 (2007), 58–71.
Cook, K.S., Hardin, R., Levi, M., Cooperation Without Trust?. 2005, Russell Sage Foundation.
Ismail, L., Hameed, H., AlShamsi, M., AlHammadi, M., AlDhanhani, N., Towards a blockchain deployment at UAE university: Performance evaluation and blockchain taxonomy. Proceedings of the 2019 International Conference on Blockchain Technology, 2019, ACM, Honolulu HI USA, 30–38, 10.1145/3320154.3320156.
Lamport, L., Shostak, R., Pease, M., The Byzantine generals problem. Concurrency: The Works of Leslie Lamport, 2019, Association for Computing Machinery, New York, NY, USA, 203–226, 10.1145/3335772.3335936.
Perera, C., Liu, C.H., Jayawardena, S., The emerging internet of things marketplace from an industrial perspective: A survey. IEEE Trans. Emerg. Top. Comput. 3:4 (2015), 585–598 URL https://ieeexplore.ieee.org/document/7004800.
Determann, L., No one owns data. UC Hast. Law, 70, 2018, 44, 10.2139/ssrn.3123957.
Nord, J.H., Koohang, A., Paliszkiewicz, J., The internet of things: Review and theoretical framework. Expert Syst. Appl. 133 (2019), 97–108, 10.1016/j.eswa.2019.05.014 URL https://www.sciencedirect.com/science/article/pii/S0957417419303331.
S. Arumugam, R. Bhargavi, 2019. A survey on driving behavior analysis in usage based insurance using big data. 6 (1), 86. http://dx.doi.org/10.1186/s40537-019-0249-5. URL https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0249-5.
D.E. Pozen, 2005. The mosaic theory, national security, and the freedom of information act. 52.
Archie, M., Gershon, S., Katcoff, A., Zeng, A., Who's watching? De-anonymization of netflix reviews using amazon reviews. 2018 Online; accessed 30 December 2021. URL https://www.oasislabs.com/how-it-works.
Zagi, L.M., Aziz, B., Privacy attack on IoT: A systematic literature review. 7th International Conference on ICT for Smart Society: AIoT for Smart Society, ICISS 2020 - Proceeding, 2020, Institute of Electrical and Electronics Engineers Inc. URL https://ieeexplore.ieee.org/document/9307568.
Kondor, D., Hashemian, B., de Montjoye, Y.-A., Ratti, C., Towards matching user mobility traces in large-scale datasets. IEEE Trans. Big Data 6:4 (2020), 714–726, 10.1109/TBDATA.2018.2871693.
Wood, A., Altman, M., Bembenek, A., Bun, M., Gaboardi, M., Honaker, J., Nissim, K., O'Brien, D., Steinke, T., Vadhan, S., Differential privacy: A primer for a non-technical audience. SSRN Electron. J., 2018, 10.2139/ssrn.3338027.
Narayanan, A., Shmatikov, V., Robust de-anonymization of large sparse datasets. 2008 IEEE Symposium on Security and Privacy (Sp 2008) 1081-6011, 2008, IEEE, Oakland, CA, USA, 111–125, 10.1109/SP.2008.33.
Archie, M., Gershon, S., Katcoff, A., Zeng, A., De-anonymization of netflix reviews using amazon reviews. 2018, 5 URL https://www.readkong.com/page/de-anonymization-of-netflix-reviews-using-amazon-reviews-1439089.
Lomas, N., France fines google $120M and amazon $42M for dropping tracking cookies without consent. 2020 URL https://dataprotection.news/france-fines-google-120m-and-amazon-42m-for-dropping-tracking-cookies-without-consent/.
BBC. H&M fined for breaking GDPR over employee surveillance - BBC news. BBC, 2020 URL https://www.bbc.com/news/technology-54418936.
Marketing: the Italian SA fines TIM EUR27.8 million. 2020 URL https://edpb.europa.eu/news/national-news/2020/marketing-italian-sa-fines-tim-eur-278-million_en.
Feng, Q., He, D., Zeadally, S., Khan, M.K., Kumar, N., A survey on privacy protection in blockchain system. J. Netw. Comput. Appl. 126 (2019), 45–58, 10.1016/j.jnca.2018.10.020.
Wang, C., Zhang, N., Wang, C., Managing privacy in the digital economy. Fund. Res. 1:5 (2021), 543–551, 10.1016/j.fmre.2021.08.009.
Akil, M., Islami, L., Fischer-Hübner, S., Martucci, L.A., Zuccato, A., Privacy-preserving identifiers for IoT: A systematic literature review. IEEE Access 8 (2020), 168470–168485, 10.1109/ACCESS.2020.3023659.
Gebremichael, T., Ledwaba, L.P.I., Eldefrawy, M.H., Hancke, G.P., Pereira, N., Gidlund, M., Akerberg, J., Security and privacy in the industrial internet of things: Current standards and future challenges. IEEE Access 8 (2020), 152351–152366, 10.1109/ACCESS.2020.3016937.
Driessen, S.W., Monsieur, G., Van Den Heuvel, W.-J., Data market design: A systematic literature review. IEEE Access 10 (2022), 33123–33153, 10.1109/ACCESS.2022.3161478.
Deepa, N., Pham, Q.-V., Nguyen, D.C., Bhattacharya, S., Prabadevi, B., Gadekallu, T.R., Maddikunta, P.K.R., Fang, F., Pathirana, P.N., A survey on blockchain for big data: Approaches, opportunities, and future directions. Future Gener. Comput. Syst. 131 (2022), 209–226.
Perez, A.J., Zeadally, S., Secure and privacy-preserving crowdsensing using smart contracts: Issues and solutions. Comp. Sci. Rev., 43, 2022, 100450, 10.1016/j.cosrev.2021.100450.
Gonçalves, C., Bessa, R.J., Pinson, P., A critical overview of privacy-preserving approaches for collaborative forecasting. Int. J. Forecast. 37:1 (2021), 322–342, 10.1016/j.ijforecast.2020.06.003.
Wu, Y., Wang, Z., Ma, Y., Leung, V.C.M., Deep reinforcement learning for blockchain in industrial IoT: A survey. Comput. Netw., 191, 2021, 108004, 10.1016/j.comnet.2021.108004 URL https://www.sciencedirect.com/science/article/pii/S1389128621001213.
Nguyen, L.D., Leyva-Mayorga, I., Lewis, A.N., Popovski, P., Modeling and analysis of data trading on blockchain-based market in IoT networks. IEEE Internet Things J. 8:8 (2021), 6487–6497, 10.1109/JIOT.2021.3051923.
Sadiq, A., Javed, M.U., Khalid, R., Almogren, A., Shafiq, M., Javaid, N., Blockchain based data and energy trading in internet of electric vehicles. IEEE Access 9 (2021), 7000–7020, 10.1109/ACCESS.2020.3048169.
Long, Y., Chen, Y., Ren, W., Dou, H., Xiong, N.N., DePET: A decentralized privacy-preserving energy trading scheme for vehicular energy network via blockchain and K-anonymity. IEEE Access 8 (2020), 192587–192596, 10.1109/ACCESS.2020.3030241.
Xu, R., Chen, Y., Fed-DDM: A federated ledgers based framework for hierarchical decentralized data marketplaces. 2021 International Conference on Computer Communications and Networks, 2021.
Giaretta, L., Savvidis, I., Marchioro, T., Girdzijauskas, S., Pallis, G., Dikaiakos, M.D., Markatos, E., PDS2: A user-centered decentralized marketplace for privacy preserving data processing. 2021 IEEE 37th International Conference on Data Engineering Workshops (ICDEW), 2021, 92–99, 10.1109/ICDEW53142.2021.00024.
Manzoor, A., Braeken, A., Kanhere, S.S., Ylianttila, M., Liyanage, M., Proxy re-encryption enabled secure and anonymous IoT data sharing platform based on blockchain. J. Netw. Comput. Appl., 176, 2021, 102917, 10.1016/j.jnca.2020.102917.
Rückel, T., Sedlmeir, J., Hofmann, P., Fairness, integrity, and privacy in a scalable blockchain-based federated learning system. Comput. Netw., 202, 2022, 108621, 10.1016/j.comnet.2021.108621.
Gupta, P., Dedeoglu, V., Kanhere, S.S., Jurdak, R., TrailChain: Traceability of data ownership across blockchain-enabled multiple marketplaces. J. Netw. Comput. Appl., 2022, 103389, 10.1016/j.jnca.2022.103389.
Tian, Y., Song, B., Ma, T., Al-Dhelaan, A., Al-Dhelaan, M., Bi-tier differential privacy for precise auction-based people-centric IoT service. IEEE Access 9 (2021), 55036–55044, 10.1109/ACCESS.2021.3067138.
Zhang, M., Yang, L., He, S., Li, M., Zhang, J., Privacy-preserving data aggregation for mobile crowdsensing with externality: An auction approach. IEEE/ACM Trans. Netw. 29:3 (2021), 1046–1059, 10.1109/TNET.2021.3056490.
Kserawi, F., Al-Marri, S., Malluhi, Q., Privacy-preserving fog aggregation of smart grid data using dynamic differentially-private data perturbation. IEEE Access 10 (2022), 43159–43174, 10.1109/ACCESS.2022.3167015.
Shen, Y., Guo, B., Shen, Y., Duan, X., Dong, X., Zhang, H., Zhang, C., Jiang, Y., Personal big data pricing method based on differential privacy. Comput. Secur., 113, 2022, 102529, 10.1016/j.cose.2021.102529.
Hu, Y., Li, C., Hu, A., Hu, A., Zhao, J., Trading off data resource availability and privacy preservation in multi-layer network transaction. Phys. Commun., 46, 2021, 101317, 10.1016/j.phycom.2021.101317.
Song, Q., Cao, J., Sun, K., Li, Q., Xu, K., Try before you buy: Privacy-preserving data evaluation on cloud-based machine learning data marketplace. Annual Computer Security Applications Conference, 2021, ACM, 260–272, 10.1145/3485832.3485921.
M.N. Alraja, H. Barhamgi, A. Rattrout, M. Barhamgi, 2021. An integrated framework for privacy protection in IoT — Applied to smart healthcare. 91, 107060. http://dx.doi.org/10.1016/j.compeleceng.2021.107060. URL https://www.sciencedirect.com/science/article/pii/S0045790621000744.
Oppliger, R., Privacy-enhancing technologies for the world wide web. Comput. Commun. 28:16 (2005), 1791–1797, 10.1016/j.comcom.2005.02.003.
Pedersen, T.P., Non-interactive and information-theoretic secure verifiable secret sharing. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) LNCS, vol. 576, 1992, 129–140, 10.1007/3-540-46766-1_9.
Shamir, A., How to share a secret. Publ. ACM, 1979, 10.1007/978-3-642-15328-0_17.
Chen, X., Introduction to Secure Outsourcing Computation. 2016, Morgan & Claypool publishers, 94 URL https://www.researchgate.net/publication/295681472_Introduction_to_Secure_Outsourcing_Computation.
De Capitani Di Vimercati, S., Foresti, S., Livraga, G., Samarati, P., Data privacy: Definitions and techniques. Int. J. Uncertain. Fuzz. Knowl.-Based Syst. 20:6 (2012), 793–817, 10.1142/S0218488512400247 URL https://www.worldscientific.com/doi/abs/10.1142/S0218488512400247.
Meyerson, A., Williams, R., On the complexity of optimal k-anonymity. Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, 23, 2004, 223–228, 10.1145/1055558.1055591 URL https://dl.acm.org/doi/10.1145/1055558.1055591.
Rivest, R., Shamir, A., Tauman, Y., How to leak a secret. Lecture Notes in Computer Science, Vol 2248, 2001, Springer, Berlin, Heidelberg URL https://ieeexplore.ieee.org/document/6032224%0Ahttps://cryptoslate.com/ethereum-network-congestion-doubles-gas-fees-as-game.
Bellare, M., Micciancio, D., Warinschi, B., Foundations of group signatures: Formal definitions, simplified requirements, and a construction based on general assumptions. Eurocrypt 2656 (2003), 1–27 URL https://cseweb.ucsd.edu/~mihir/papers/gs.pdf.
Yung, M., Jarecki, S., Krawczyk, H., Herzberg, A., Proactive secret sharing or: How to cope with perpetual leakage. Communication, 1995 URL https://www.researchgate.net/publication/221355399_Proactive_Secret_Sharing_Or_How_to_Cope_With_Perpetual_Leakage.
IOTA-Foundation. About the tangle. 2020 URL https://legacy.docs.iota.org/docs/getting-started/1.1/the-tangle/overview.
Ghosh, A., Ligett, K., Roth, A., Schoenebeck, G., Buying private data without verification. EC 2014 - Proceedings of the 15th ACM Conference on Economics and Computation, 2014, 931–948, 10.1145/2600057.2602902 arXiv:1404.6003.
Perera, C., Ranjan, R., Wang, L., Khan, S.U., Zomaya, A.Y., Big data privacy in the internet of things era. IT Prof. 17:3 (2015), 32–39, 10.1109/MITP.2015.34.
The Wall Street Journal. Google to pay $22.5 million in FTC settlement. 2012 URL https://www.wsj.com/articles/SB10000872396390443404004577579232818727246.
Porter, J., Google fined €50 million for GDPR violation in France. 2019 URL https://www.theverge.com/2019/1/21/18191591/google-gdpr-fine-50-million-euros-data-consent-cnil.
G. Goos, J. Hartmanis, J. van Leeuwen, D. Hutchison, T. Kanade, J. Kittler, J.M. Kleinberg, F. Mattern, J.C. Mitchell, M. Naor, O. Nierstrasz, C.P. Rangan, B. Steffen, 1973. Lecture notes in computer science. 556. URL https://doi.org/10.1007/978-3-319-70139-4_56.
Cramton, P., Shoham, Y., Steinberg, R., An overview of combinatorial auctions. ACM SIGECOM Exch. 7:1 (2007), 3–14 URL https://dl.acm.org/doi/10.1145/1345037.1345039.
Dingledine, R., Mathewson, N., Syverson, P., Tor: The Second-Generation Onion Router: Tech. Rep., 2004, Defense Technical Information Center, Fort Belvoir, VA URL http://www.dtic.mil/docs/citations/ADA465464.
D. Bogdanov, S. Laur, J. Willemson, 2008. Sharemind: a framework for fast privacy-preserving computations. 15. URL https://link.springer.com/chapter/10.1007/978-3-540-88313-5_13.
Wang, N., Xiao, X., Yang, Y., Zhao, J., Hui, S.C., Shin, H., Shin, J., Yu, G., Collecting and analyzing multidimensional data with local differential privacy. 2019, 13 URL https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8731512.
Bassily, R., Smith, A., Local, private, efficient protocols for succinct histograms. Proceedings of the Forty-Seventh Annual ACM Symposium on Theory of Computing, 2015, ACM, 10.1145/2746539.2746632.
Herzberg, A., Jarecki, S., Krawczyk, H., Yung, M., Proactive secret sharing or: How to cope with perpetual leakage. Coppersmith, D., (eds.) Advances in Cryptology — CRYPT0’ 95, 1995, Springer Berlin Heidelberg, Berlin, Heidelberg, 339–352 URL https://link.springer.com/chapter/10.1007/3-540-44750-4_27.
Bellet, A., Habrard, A., Sebban, M., A survey on metric learning for feature vectors and structured data. 2014 URL https://arxiv.org/abs/1306.6709.
Poettering, B., Stebila, D., Double-authentication-preventing signatures. Int. J. Inf. Secur., 16(1), 2017 URL http://link.springer.com/10.1007/s10207-015-0307-8.
Yu, S., Wang, C., Ren, K., Lou, W., Achieving secure, scalable, and fine-grained data access control in cloud computing. 2010 Proceedings IEEE INFOCOM, 2010, 1–9, 10.1109/INFCOM.2010.5462174.
Wieringa, R., Maiden, N., Mead, N., Rolland, C., Requirements engineering paper classification and evaluation criteria: A proposal and a discussion. Requir. Eng. 11:1 (2006), 102–107, 10.1007/s00766-005-0021-6.